Combining wave energy with wind and solar: Short-term forecasting

Reikard, Gordon; Robertson, Bryson; Bidlot, Jean‐Raymond

doi:10.1016/j.renene.2015.03.032

Cited by 98 publications

(40 citation statements)

References 43 publications

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“…G. Reikard et al studied the variabilities of solar, wind, and wave energy for integration to the grid, with a forecasting system proposed for the three sources based on a regression method. Wave energy is shown to be more predictable than solar and wind, due to the strong weather impact for the latter two sources [65]. Recently, a review of solar and wind space-time variabilities has been conducted by K. Engeland et al but this does not include tidal and wave resources [66].…”

Section: Intermittency and Variability Comparisonmentioning

confidence: 99%

“…Recently, a review of solar and wind space-time variabilities has been conducted by K. Engeland et al but this does not include tidal and wave resources [66]. Table 1 presents the main characteristics in terms of variability and intermittency for each source, with the origin and existing methods to evaluate temporal variations according to different publications [61][62][63][64][65][66]. …”

Section: Intermittency and Variability Comparisonmentioning

confidence: 99%

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Electrical Power Supply of Remote Maritime Areas: A Review of Hybrid Systems Based on Marine Renewable Energies

et al. 2018

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Abstract:Ocean energy holds out great potential for supplying remote maritime areas with their energy requirements, where the grid size is often small and unconnected to a continental grid. Thanks to their high maturity and competitive price, solar and wind energies are currently the most used to provide electrical energy. However, their intermittency and variability limit the power supply reliability. To solve this drawback, storage systems and Diesel generators are often used. Otherwise, among all marine renewable energies, tidal and wave energies are reaching an interesting technical level of maturity. The better predictability of these sources makes them more reliable than other alternatives. Thus, combining different renewable energy sources would reduce the intermittency and variability of the total production and so diminish the storage and genset requirements. To foster marine energy integration and new multisource system development, an up-to-date review of projects already carried out in this field is proposed. This article first presents the main characteristics of the different sources which can provide electrical energy in remote maritime areas: solar, wind, tidal, and wave energies. Then, a review of multi-source systems based on marine energies is presented, concerning not only industrial projects but also concepts and research work. Finally, the main advantages and limits are discussed.

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Section: Intermittency and Variability Comparisonmentioning

confidence: 99%

Section: Intermittency and Variability Comparisonmentioning

confidence: 99%

Electrical Power Supply of Remote Maritime Areas: A Review of Hybrid Systems Based on Marine Renewable Energies

et al. 2018

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“…WECs are used to extract energy from ocean waves, and are therefore ment to play an important role in providing a sustainable energy source that can be more accurately forecasted than wind and solar energy [15]. From the Edinburgh Duck, one of the earliest and most well known modern WEC, a lot of different designs have been developed, but in general they can be categorized in three types: Attenuator, Point absorber and Terminators [16].…”

Section: Identical Energy Convertersmentioning

confidence: 99%

A Model Free Control Based on Machine Learning for Energy Converters in an Array

Thomas

Giassi

Göteman

et al. 2018

BDCC

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This paper introduces a machine learning based control strategy for energy converter arrays designed to work under realistic conditions where the optimal control parameter can not be obtained analytically. The control strategy neither relies on a mathematical model, nor does it need a priori information about the energy medium. Therefore several identical energy converters are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each situation. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converters (WECs) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

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“…Among the ways in which electricity can be generated from renewable-energy sources (RES), the one based on wind turbines is believed to be the least harmful to the environment [30][31][32]. It has also been the most cost effective of the RES, until now [33].…”

Section: Introductionmentioning

confidence: 99%